Multimedia data mining and searching through dynamic index evolution

Clement H C LEUNG*, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)


While the searching of text document has grown relatively mature on the Internet, the searching of images and other forms of multimedia data significantly lags behind. To search visual information on the basis of semantic concepts requires both their discovery and meaningful indexing. By analyzing the users' search, relevance feedback and selection patterns, we propose a method which allows semantic concepts to be discovered and migrated through an index hierarchy. Our method also includes a robust scoring mechanism that permits faulty indexing to be rectified over time. These include: (i) repeated and sustained corroboration of specific index terms before installation, and (ii) the ability for the index score to be both incremented and decremented. Experimental results indicate that convergence to an optimum index level may be achieved in reasonable time periods through such dynamic index evolution.

Original languageEnglish
Title of host publicationAdvances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540764137
Publication statusPublished - 2007
Event9th International Conference on Visual Information Systems, VISUAL 2007 - Shanghai, China
Duration: 28 Jun 200729 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Visual Information Systems, VISUAL 2007

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Dynamic indexing
  • Evolution strategy
  • Information recovery
  • Multimedia data mining
  • Relevance feedback
  • Semantic search
  • Web intelligence


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